Biomarkers
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Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin Antonio Lacquaniti, Chiara Caccamo, Paola Salis, Valeria Chirico, Antoine Buemi, Valeria Cernaro, Alberto Noto, Giuseppina Pettinato, Domenico Santoro, Tullio Bertani, Michele Buemi & Antonio David To cite this article: Antonio Lacquaniti, Chiara Caccamo, Paola Salis, Valeria Chirico, Antoine Buemi, Valeria Cernaro, Alberto Noto, Giuseppina Pettinato, Domenico Santoro, Tullio Bertani, Michele Buemi & Antonio David (2016) Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin, Biomarkers, 21:4, 371-378, DOI: 10.3109/1354750X.2016.1141991 To link to this article: http://dx.doi.org/10.3109/1354750X.2016.1141991
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Date: 11 May 2016, At: 23:09
http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2016; 21(4): 371–378 ! 2016 Taylor & Francis. DOI: 10.3109/1354750X.2016.1141991
RESEARCH ARTICLE
Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin Antonio Lacquaniti1#, Chiara Caccamo2, Paola Salis2, Valeria Chirico3, Antoine Buemi4, Valeria Cernaro1, Alberto Noto5, Giuseppina Pettinato1, Domenico Santoro1, Tullio Bertani1, Michele Buemi1, and Antonio David5 Department of Internal Medicine, University Hospital of Messina, Messina, Italy, 2Department of Internal Medicine, Mediterranean Institute for Transplantation and Advanced Specialized Therapies, ISMETT, University of Pittsburgh Medical Center, Palermo, Italy, 3Department of Pediatric Science, University Hospital of Messina, Messina, Italy, 4Surgery and Abdominal Transplantation Division, Cliniques Universitaires Saint-Luc, Universite´ Catholique De Louvain, Brussels, Belgium, and 5Department of Neuroscience, University Hospital of Messina, Messina, Italy
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Abstract
Keywords
Context: Available markers are not reliable parameters to early detect kidney injury in transplanted patients. Objective: Examine neutrophil gelatinase associated lipocalin (NGAL) in early detection of delayed graft function (DGF) and as a long-term predictor of graft outcome. Patients and methods: NGAL was evaluated in 124 transplanted patients. Results: Urinary NGAL levels were associated to a 10% (HR: 1.10; 95% CI: 1.04–1.25; p50.001) and 15% (HR: 1.15; 95% CI: 1.09–1.26; p50.001) increased risk of DGF and allograft nephropathy progression, respectively. Conclusion: NGAL reflects the entity of renal impairment in transplanted patients, representing a biomarker and an independent risk factor for DGF and chronic allograft nephropathy progression.
Biomarker, chronic allograft nephropathy, delayed graft function, kidney transplantation, long-term graft failure, neutrophil gelatinase associated lipocalin
Introduction Early assessment of kidney function following renal transplantation is crucial for predicting graft survival, but not by serum creatinine, a not reliable parameter to detect acute kidney injury. In fact, the time relationship between changes in serum creatinine and concomitant changes in glomerular filtration rate (GFR) does not allow accurately estimating timing of injury and severity of dysfunction (Vanmassenhove et al., 2013). Furthermore, definitions of delayed graft function (DGF) are not standardized; in recent years, serum creatinine levels, its reduction rate and urine output have been used to identify DGF on different days following transplantation (Mallon et al., 2013). Several clinical algorithms have been proposed for the prediction of DGF based on pre-operative risk factors, but no objective tools are currently available (Gourishankar et al., 2013). However, 1-year graft survival after kidney transplant has greatly improved over the years, whereas long-term graft and
#Antonio Lacquaniti is responsible for statistical design/analysis. E-mail: [email protected] Address for correspondence: Dr Antonio Lacquaniti, Department of Internal Medicine, 98100 Messina, Italy. Tel: +39 090 2212396. E-mail: [email protected]
History Received 8 June 2015 Revised 7 January 2016 Accepted 11 January 2016 Published online 11 February 2016
patient survival has changed to only a limited extent (Hariharan et al., 2000; Lachenbruch et al., 2004). As chronic transplant dysfunction is a predominantly tubular-interstitial process in kidney transplant recipients, specific tubular damage markers, such as kidney injury molecule-1 (KIM-1) or neutrophil gelatinase-associated lipocalin (NGAL), could be better predictors of kidney function outcome. In particular, NGAL is a protein belonging to the lipocalin superfamily produced by activated neutrophils. It has been shown that many other cells, including kidney tubule, may be involved in NGAL production, in response to several injuries, such as ischemia-reperfusion, sepsis or toxic stress (Bolignano et al., 2008, 2010). Haase confirmed the predictive and prognostic value of NGAL as an early biomarker for acute damage in a metaanalysis involving more than 2500 patients (Haase et al., 2009). Furthermore, NGAL evaluation was also applied in other several clinical settings of acute kidney injury, such as contrast induced nephropathy or after major surgery (Lacquaniti et al., 2013a,b). In the field of kidney transplantation, numerous parameters have been used to predict graft survival, including NGAL (Buemi et al., 2014; Hollmen et al., 2011; Neri et al., 2012). However, while few studies demonstrated its utility in predicting DGF, it is unknown whether it would predict long-term allograft function after renal transplantation.
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Several data have in fact showed that NGAL represents not only a biomarker of acute renal injury, but also it plays a diagnostic and prognostic role in chronic kidney diseases (Bolignano et al., 2009; Lacquaniti et al., 2013c). Moreover, Pennemans analyzing urine NGAL values in healthy subjects, proposed physiological cut-off values, according to gender and age. It was in fact demonstrated that urine NGAL is closely related to patient’s age and sex, also after its correction for creatinine and urine specific gravity (Pennemans et al., 2013). In this study, we examined if urine and serum NGAL could detect acute kidney damage after renal transplantation, assessing its role to early reveal DGF. We have also evaluated the role of NGAL as long-term predictor of graft outcome.
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Materials and methods
Biomarkers, 2016; 21(4): 371–378
Collection of blood and NGAL evaluation In DGF group, serum and urine samples were collected preoperatively (baseline data) and at the first post-operative day (POD 1). Neutrophil gelatinase associated lipocalin (NGAL) was measured in blood and urine using ELISA commercial available kit (Antibody Shop, Gentofte, Denmark) according to manufacturer’s instructions. All specimens were often diluted to obtain concentration for the optimal density according to ELISA kit instructions. The enzymatic reactions were quantified in an automatic microplate photometer. Serum (sNGAL) and urinary NGAL (uNGAL) levels were expressed as ng/ml. In CAN group, sNGAL and uNGAL levels were evaluated at the enrollment phase. Patients were followed every 3 months, through the evaluation of renal function tests and other common biochemical parameters, including 24-h urine samples.
Patients and study design
Definitions and follow-up period
A prospective and observational study was designed, involving 124 patients. In particular, 40 transplanted patients, 418 years of age, who received a kidney from deceased and living donors at the Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Palermo, Italy, were enrolled to evaluate delay graft function (DGF group). To minimize potential confounding factors, exclusion criteria were neoplastic diseases, pregnancy, cardio-renal syndrome, acute hepatitis and cirrhosis, recent stroke. We excluded patients who had a primary graft failure related to surgical/anatomic causes. The immunosuppressive regimen was similar in all patients, consisting of pre-operative basiliximab and post-operative tacrolimus with prednisone and mycophenolate mofetil. Eighty-four renal transplanted patients who were referred to the renal transplant outpatient clinic of the Nephrology and Dialysis Unit of Messina University Hospital were enrolled to assess the chronic allograft nephropathy progression (CAN group). In particular, patients with 12 months or more renal transplantation were eligible to begin a 5-year observational study. The study recruited male or female patients aged 18–70 years who received first or second kidney transplant from a deceased (CTx), living-related (LTx) or living-unrelated donor, with panel reactive antibodies (PRA) below 20% at the last pre-transplant assessment. Patients were excluded if they received multi-organ transplant (including two kidneys) or previous non-renal transplant. Patients with serum creatinine above 6 mg/dl and/or estimated GFR515 ml/min, malignancy, liver, thyroid or infectious diseases, severe proteinuria (43 g/die), active inflammatory states, moderate-severe heart disease were not recruited. The Ethics Committee and the Institutional review board approved the study. The research was performed in accordance with the Declaration of Helsinki. The clinical and research activities have been consistent with the Principles of the Declaration of Istanbul. All patients gave written informed consent.
Cold ischemia time (CIT) was defined as the duration between the beginning of cold storage and reperfusion of the graft. CIT was grouped as 0–15 h and 415 h for analysis. Delayed graft function (DGF) was defined by need for dialysis within the first week after transplantation, or when serum creatinine level increased, remained unchanged, or decreased by less than 10% per day immediately after surgery (Yarlagadda et al., 2008). Glomerular filtration rate (GFR) post-transplantation time points were calculated using the modification of diet in renal disease (MDRD) equation (Levey et al., 1999). According with these definitions, patients were separated into two groups: DGF group and immediate graft function (IGF) group. Chronic allograft nephropathy progression (CAN) group patients were followed prospectively until the end of 5-year period or the primary study endpoint was reached. This latter was defined by the loss of the renal graft, defined as a re-start of dialytic treatment, a decline of the GFR515 ml/min, a re-transplant or a reduction in kidney function revealed by a doubling of serum creatinine, an accepted surrogate index of GFR slope (Rossing, 1998). Statistical analyses Statistical analyses were performed with NCSS for Windows version 4.0, the MedCalc version 11.0 (MedCalc Software Acacialaan, Ostend, Belgium) software and the GraphPad Prism version 5.0 (GraphPad Software, Inc., San Diego, CA) package. Data were presented as mean ± SD for normally distributed values and median [IQ range] for non-normally distributed values. Differences between groups were established by unpaired t test or by ANOVA followed by Bonferroni’s test for normally distributed values and by the Kruskal–Wallis analysis followed by Dunn’s test for nonparametric values. Dichotomized values were compared using the 2 test. Correlation coefficients were used as appropriate to test correlations between NGAL and other variables. Receiver-operating characteristics (ROC) analysis was employed to calculate the area under the curve (AUC) for serum and urine NGAL, creatinine and urinary output to find
DOI: 10.3109/1354750X.2016.1141991
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the best cut off values capable for identifying DGF and the progression to renal endpoint. Kaplan–Meier curves were generated to assess renal survival. Differences in the survival rates were evaluated using the log-rank test. Adjusted risk estimates for progression endpoint were calculated using univariate followed by multivariate Cox proportional hazard regression analysis. All results were considered significant if p was50.05.
Results NGAL as a diagnostic marker of DGF
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Patients characteristics at baseline and after kidney transplant In DGF group, 30 (75%) patients received hemodialysis whereas 3 (7%) received peritoneal dialysis in the pre-surgery period. Seven (18%) patients did not receive pre-transplantation dialysis. The mean time on dialysis was 27 ± 4.4 months. Living donor transplantation accounted for 27% (11 patients), whereas 29 patients (73%) underwent deceased kidney transplant. The cold ischemia time was 12.9 ± 5.4 h. Twenty-eight patients (69%) transplanted within 15 h, whereas the remaining (12 patients; 31%) received the kidney after a cold ischemia time 415 h. Delayed graft function (DGF) was encountered in 22 (55%) recipients, while no LTx patients had a DGF event after transplantation. At baseline, before the transplantation, sNGAL level was 386.9 ± 39.9 ng/ml, with no differences between CTx and LTx (387.1 ± 43 versus 374.1 ± 20.1 ng/ml, respectively; p ¼ 0.20). At POD 1, NGAL levels were lower in LTx than CTx patients (sNGAL: 223.1 ± 86.7 versus 392.1 ± 225.3 ng/ml; p50.001; uNGAL: 39.6 ± 13.9 versus 172.5 ± 117.7 ng/ml; p50.001). Moreover, sNGAL and uNGAL levels were higher in patients who received a deceased donor kidney and subsequently developed delayed graft function (DGF group: 520.7 ± 318 ng/ml and 135.8 ± 93.4, respectively), compared with CTx patients with prompt graft function (IGF group: 287.8 ± 162.2 ng/ml; p50.001 and 47.4 ± 40.3; p50.001). Figure 1 summarizes these findings. Serum creatinine was not different between IGF and DGF groups at POD 1 (p ¼ 0.10), whereas it was higher in DGF group than IGF patients only after 3 days from renal transplant (p50.001). A close correlation between NGAL and renal function parameters, such as creatinine and eGFR, was assessed. Moreover, a strict and direct correlation between uNGAL and CIT was revealed. Table 1 demonstrates clinical characteristics of donors and recipients, at baseline and after kidney transplantation. ROC analyses Receiver operating characteristics (ROC) analyses were performed in order to define the diagnostic profile of NGAL in identifying DGF among transplanted patients. The AUC for uNGAL was 0.979. Moreover, a cut-off value of uNGAL set at 105 ng/mL was associated to a sensitivity and specificity of 95.8 and 91.9%.
Figure 1. Serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) levels in transplanted patients enrolled for DGF evaluation. (A) While no differences were assessed in pre-transplant period (NS), both serum and urine NGAL levels were reduced in related living transplant patients (LTx) in the first post-operative day. (B) Serum and urine NGAL levels were higher in patients who received a deceased donor kidney (CTx) and subsequently developed delayed graft function (DGF group), compared with CTx patients with prompt graft function (IGF group).
Diagnostic performances of uNGAL were better than creatinine (AUC: 0.676; p50.001), whereas no differences have been revealed between creatinine and sNGAL. Urine output of the first 24 h was characterized by an AUC of 0.880, with a more sensitivity and specificity than creatinine and sNGAL (p50.001 and 0.002, respectively), but less than uNGAL (p ¼ 0.02) to detect DGF (Figure 2). Univariate/multiple Cox regression analysis and risk of DGF To identify putative risk factors associated with DGF, we performed a Cox regression analysis, inserting in the model all variables that were different at first post-operative day in patients who had or not a DGF. Univariate analysis showed that only donor age, donor creatinine, CIT, HLA mismatch, residual diuresis, sNGAL and uNGAL were significantly associated with endpoint, whereas recipient age and time on dialysis failed to reach statistical significance. A multiple Cox regression indicated that both uNGAL and sNGAL predicted high risk of DGF, independently of others variables. In detail, the increase of uNGAL was associated with a 10% increased risk of DGF (HR 1.10; 95% CI: 1.04–1.25; p50.001), whereas the increase of sNGAL
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Table 1. Demographic and clinical characteristics in transplant recipients and donors.
Deceased donor Age (years) Gender, male/female Inotropic support, n Hypertension, n (%) Diabetes mellitus Terminal serum creatinine CIT, h Recipient Age, years Gender, male/female BMI, kg/m2 Diabetes mellitus Blood Hypertension, n (%) HLA mismatch 5 3, n (%) HLA mismatch 3, n (%) Time on dialysis, months Residual diuresis, ml/24 h Baseline creatinine, mg/dl Baseline eGFR, ml/min POD 1 sNGAL, ng/ml POD 1 uNGAL, ng/ml
Total (n ¼ 29)
DGF (n ¼ 22)
No DGF (n ¼ 7)
p
47.3 ± 20.8 17/12 18 8 (27) 0 1.21 ± 0.6 12.9 ± 5.4
63.8 ± 7.7 13/9 13 6 – 1.3 ± 0.7 16.1 ± 4.1
28.4 ± 12.9 4/3 5 2 – 0.8 ± 0.2 7.6 ± 1.5
50.0001 0.34 0.57 0.94 – 0.007 50.0001
45.2 ± 18.3 15/14 23 ± 5.5 4 (14%) 15 (51%) 10 (34%) 19 (66%) 27 ± 4.4 311.1 ± 428.1 8.7 ± 1.2 8.6 ± 1.2 392.1 ± 225.3 172.5 ± 117.7
58.7 ± 7.8 15/7 23.1 ± 5.1 3 11 4 (18%) 18 (82%) 29.0 ± 3.1 214.2 ± 413.8 8.6 ± 1.2 8.5 ± 2.5 520.7 ± 318 135.8 ± 93.4
27.3 ± 9.8 2/5 22.6 ± 7.3 1 4 6 (85%) 1 (15%) 21.7 ± 2.5 700 ± 305.5 9.0 ± 1.5 9.0 ± 1.6 287.8 ± 162.2 47.4 ± 40.3
0.0002 0.41 0.92 0.96 0.75 0.0007 0.0004 50.0001 0.005 0.48 0.61 0.007 0.0003
DGF: delayed graft function; CIT: cold ischemia time; BMI: body mass index; HLA: human leukocyte antigen; eGFR: estimated glomerular filtration rate; POD: post-operative day; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
amplified this risk by 6% (HR 1.06; 95% CI: 1.03–1.12; p50.001). Table 2 summarizes the Cox analysis. NGAL as a prognostic marker for allograft nephropathy progression NGAL evaluation
Figure 2. Receiver operating characteristics curves of serum neutrophil gelatinase-associated lipocalin (sNGAL), urinary NGAL (uNGAL), serum creatinine and urinary output considering delayed graft function (DGF) as status variable. The area under the curve for sNGAL, uNGAL, serum creatinine and urinary output was 0.67 (95% CI: 0.54–0.79), 0.97 (95% CI: 0.90–0.99), 0.67 (95% CI: 0.54–0.79) and 0.88 (95% CI: 0.77–0.94), respectively. The best cut-off value to predict DGF for uNGAL was found to be 105 ng/ml, with a sensitivity of 95.8 (95% CI: 78.9–99.9) and a specificity of 91.9 (95% CI: 78.1–98.3), whereas for urinary output it was 15 ml/h with a sensitivity of 64.0 (95% CI: 42.5– 82.0) and a specificity of 97.3 (95% CI: 85.8–99.9). Urine output of the first 24 h was characterized by a more sensitivity and specificity than creatinine and sNGAL (p50.001 and p ¼ 0.002, respectively), but less than uNGAL (p ¼ 0.02) to detect DGF.
Eighty-four renal transplanted patients were enrolled to assess chronic allograft nephropathy progression (CAN group). Mean serum creatinine at baseline was 1.53 ± 0.7 mg/dl, with a mean eGFR of 49.7 ± 21 ml/min/1.73 m2. According to Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines based on eGFR, 18 patients (22%) belonged to stages I– II, 45 subjects (53%) to stage III and 21 (25%) patients to stage IV. Mean value of sNGAL was 137.7 ± 64.6 ng/ml, whereas uNGAL levels were 88.3 ± 72.5 ng/ml, both gradually rising according to CKD stages. In particular, the highest values, both for urine and serum NGAL, were assessed in patients belonging to stage IV (sNGAL: 197 ± 43 ng/ml; uNGAL: 112.2 ± 83 ng/ml). During the observational period, 38 patients (45%) reached the renal endpoint. In particular, 27 patients had a progression of renal disease, whereas 11 patients experienced a severe worsening in renal function, requiring dialytic treatment. Table 3 displays main data and statistical differences between patients with or without renal disease progression. ROC analysis Receiver operating characteristics (ROC) analyses were performed in order to define the diagnostic profiles of NGAL in identifying a worsening of renal function among
Delayed graft function and chronic allograft nephropathy
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Table 2. Univariate and multivariate Cox proportional hazards regression model for incidence of delayed graft function. Univariate analysis
Donor age Donor creatinine CIT HLA mismatch Recipient age Time on dialysis Residual diuresis sNGAL uNGAL
Multivariate analysis
HR
95% CI
p
HR
95% CI
p
1.03 1.02 1.11 1.09 1.06 1.15 1.02 1.04 1.08
1.01–1.06 1.01–1.03 1.03–1.21 1.05–1.17 0.97–1.08 0.73–1.18 1.01–1.04 1.01–1.08 1.08–1.22
0.02 0.03 0.002 0.001 0.12 0.23 0.04 0.001 0.004
1.02 1.03 1.14 1.08
1.01–1.03 0.96–1.03 1.05–1.27 1.04–1.10
0.01 0.10 0.004 0.01
1.01 1.06 1.10
0.97–1.04 1.03–1.12 1.04–1.25
0.09 0.006 0.0004
CIT: cold ischemia time; HLA: human leukocyte antigen; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
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Table 3. Baseline characteristics and long-term follow up in chronic allograft nephropathy group.
Recipients age, years Gender, male/female Diabetes mellitus, n (%) Hypertension, n (%) BMI, kg/m2 Months after Txa Living donation, n (%) Donor age, years CIT, h HLA mismatch 3 DGF, n (%) Acute rejection, n (%) Serum creatinine, mg/dl eGFR, ml/min b2 microglubulin Proteinuria, mg/24 h sNGAL, ng/ml uNGAL, ng/ml
Baseline (N ¼ 84)
Progressor (N ¼ 38)
Non-progressor (N ¼ 46)
p
56.1 ± 11.8 46/38 23 (27%) 68 (80%) 26.1 ± 2.3 22 (16–36) 22 (26) 58.3 ± 16.3 14.2 ± 6.3 31 (37%) 48 (57%) 56 (66%) 1.53 ± 0.7 49.7 ± 21 4727.9 ± 2985 852.3 ± 622.7 137.7 ± 64.6 88.3 ± 72.5
65.2 ± 5.7 21/17 13 (34) 33 (86%) 25.9 ± 2.5 48 (19–55) 6 (15) 65.9 ± 2.9 16.7 ± 1.6 20 (52) 29 (76) 29 (76) 1.75 ± 1.1 25.9 ± 10.2 4876.8 ± 1476.8 1090 ± 667.8 154.5 ± 52.5 128 ± 34.2
47 ± 8.6 25/21 10 (21) 35 (76) 26.3 ± 2.1 21.5 (16–29.5) 16 (35) 53.4 ± 14.4 11.9 ± 3.2 11 (24) 19 (41) 27 (58) 1.41 ± 0.3 53.5 ± 13.9 2294.6 ± 851.3 614 ± 475.4 109.7 ± 47.6 78.7 ± 44.9
0.0002 0.16 0.84 0.21 0.80 0.03 0.04 0.0008 50.0001 0.006 0.001 0.09 0.38 0.0006 0.0003 0.003 0.004 50.0001
a
Expressed as median (IQ range). BMI: body mass index; CIT: cold ischemia time; HLA: human leukocyte antigen; DGF: delayed graft function; eGFR: estimated glomerular filtration rate; sNGAL: serum neutrophil gelatinase associated lipocalin; uNGAL: urine neutrophil gelatinase associated lipocalin.
transplanted patients. The AUC for uNGAL was 0.889 with a sensitivity and specificity of 71.8 and 100% with a cut-off value set at 97 ng/ml. A comparison of ROC curves was also made. Whereas the difference between sNGAL and eGFR areas was not significant (p ¼ 0.68), uNGAL area was statistically different than that of eGFR (p ¼ 0.002) and sNGAL (p ¼ 0.002) (Figure 3). Kaplan–Meier curves were generated to assess renal survival in subjects with serum and urine NGAL values above and below the optimal ROC-derived cutoff levels. Subjects with sNGAL values above 122 ng/ml showed a significantly faster progression to endpoint (p ¼ 0.03), with a hazard ratio of 2.03 (95% CI: 1.06–3.89). Similarly, subjects with uNGAL values above 97 ng/ml experienced a significantly faster evolution to endpoint, with a mean follow-up time to progression of 32 months [log-rank ( 2):8.03; p50.001; hazard ratio: 2.7 (95% CI:1.3–5.4)] compared with460 months for uNGAL below the cut-off (Figure 4).
Univariate/multiple Cox regression analysis To identify putative risk factors associated with incidence of CAN progression, we performed the Cox regression analysis. Univariate analysis showed that only recipients age, HLA mismatch number, DGF, eGFR, b2 microglobulin, proteinuria, sNGAL and uNGAL were significantly associated with endpoint, whereas donors age and source, months after KTx and cold ischemia time failed to reach statistical significance. A multiple Cox regression was constructed, indicating that high levels of uNGAL were associated with a 15% increased risk of CAN progression (HR 1.15; 95% CI:1.09–1.26; p50.001), whereas the raise of sNGAL increased this risk by 8% (HR 1.08; 95% CI: 1.03–1.10; p ¼ 0.02) (Table 4).
Discussion Findings from this study clearly indicate that both serum and urine NGAL evaluation could be particularly useful in kidney allograft recipients, a nephropatic population always in
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balance between acute kidney injury risk and chronic renal damage progression. NGAL and delayed graft function
In fact, we demonstrated that DGF patients were characterized, since the first day after transplantation, by high levels of NGAL, in urine and serum. Therefore, if excellent allograft function occurred, as assessed in LTx patients, NGAL values
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Delayed graft function (DGF) reflects a post-ischemic acute tubular necrosis and all transplanted grafts, including IGF patients, are susceptible to ischemia-reperfusion injury (Heyman et al., 2010). NGAL represents a very precocious marker of DGF, predicting the evolution of graft function.
Figure 3. Receiver operating characteristics curves of eGFR, serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) considering progression of chronic allograft nephropathy as status variable. The area under the curve for eGFR, sNGAL and uNGAL was 0.64 (95% CI: 0.53–0.75), 0.62 (95% CI: 0.50–0.73) and 0.89 (95% CI: 0.79–0.95), respectively. As the difference between sNGAL and eGFR areas was not significant (p ¼ 0.68), uNGAL area was statistically different than that of eGFR (p ¼ 0.002) and sNGAL (p ¼ 0.002). For uNGAL, a sensitivity and specificity of 71.8 and 100%, respectively, were obtained with a cut off value set at 97 ng/ml.
Figure 4. Kaplan–Meier survival curves of renal end-point in CAN patients with serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) levels above and below the optimal receiver operating characteristics cut-off levels. (A) Patients with sNGAL 4122 ng/ml showed a significantly faster progression to endpoint (p ¼ 0.003, log-rank test), with a hazard ratio of 2.03 (95% CI: 1.06–3.89). (B) Subjects with uNGAL values above 97 ng/ml experienced a significantly faster evolution to endpoint, with a mean follow-up time to progression of 32 months [p50.001 log-rank test), witha hazard ratio of 2.7 (95% CI: 1.3–5.4).
Table 4. Univariate and Multivariate Cox proportional hazards regression model for incidence of chronic allograft nephropathy progression during a 5-year follow up period. Univariate analysis
Donors age Donors source Recipients age months after KTx HLA mismatcha Cold ischemia time DGF eGFR b2 microglobulin Proteinuria sNGAL uNGAL a
Multivariate analysis
HR
95% CI
p
1.03 1.10 1.05 1.06 1.10 1.04 1.06 0.83 1.12 1.05 1.06 1.10
0.91–1.06 0.95–1.21 1.02–1.10 0.97–1.18 1.03–1.18 0.96–1.08 1.01–1.13 0.74–0.96 1.06–1.17 1.01–1.07 1.01–1.10 1.08–1.22
0.11 0.32 0.01 0.37 0.001 0.10 0.01 0.002 0.001 0.03 0.01 0.001
HR
95% CI
p
1.03
0.97–1.04
0.08
1.08
1.04–1.10
0.01
1.02 0.94 1.15 1.02 1.08 1.15
1.01–1.04 0.89–0.97 1.07–1.20 0.97–1.04 1.03–1.10 1.09–1.26
0.03 0.03 0.002 0.10 0.02 0.003
Risk associated with HLA mismatch number43. KTx: kidney transplantation; HLA: human leukocyte antigen; CIT: cold ischemia time; DGF: delayed graft function; eGFR: estimated glomerular filtration rate; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
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DOI: 10.3109/1354750X.2016.1141991
Delayed graft function and chronic allograft nephropathy
decreased quickly during first 24 h post-transplant period, reflecting a more pronounced reversible short-term injury. It is important to underline that, in the immediate postoperative period, urine output may induce uNGAL dilution. In fact, a living related kidney transplant recipient, in the first hour after operation, has often a significant urine output and thus baseline post-transplant uNGAL results may be underestimated as a result of urinary dilution. However, as our ROC analysis showed, uNGAL presented a better diagnostic profile than sNGAL and urine output, reflecting a major involvement of kidney tubules than prerenal sources. Thus, urinary biomarker on POD1 not only predicts who will need dialysis within 1 week, but also could discriminate between more subtle allograft recovery patterns. Although there is no specific treatment for DGF, recognizing precociously patients who have an increased risk of developing DGF helps clinicians to optimize postoperative care and avoid nephrotoxic treatments and interventions. Neutrophil gelatinase associated lipocalin (NGAL) levels could guide perioperative fluid management, ensuring an adequate restoration and maintenance of the intravascular volume, according to renal tubule function. In fact, as an aggressive hydration has been recognized to be effective in avoiding DGF, fluid overload may also precipice the need of dialysis with the risk of hypovolemia and consequent renal ischemia. Moreover, NGAL values could guide the immunosuppressive strategy, as well as the induction protocol, the timing of steroid withdrawal in the first week after transplantation, or avoid or delay the introduction of calcineurin inhibitors. Furthermore, biopsy timing and post-biopsy therapeutic decisions could be based on NGAL levels, choosing more aggressive strategies in patients with high serum and urine NGAL values. Daily measurements of this biomarker could also give prognostic information, acting as a marker of therapy response. Further studies are needed to confirm this timing and to validate these suggested roles of NGAL. The closely relation between uNGAL and renal tubule was also demonstrated by Mori who revealed that clinical tubulitis Banff Ia/Ib and other clinical tubular pathologies caused significantly higher levels of NGAL than those assessed in stable transplants with normal tubular histology or stable transplants with subclinical tubulitis (Cernaro et al., 2011; Mori et al., 2005). Our data revealed that only uNGAL has been closely related to cold ischemia time, one of the main modifiable pretransplantation risk factors, responsible of DGF and graft survival (Debout et al., 2015). This datum was also confirmed by in vitro studies, in which NGAL staining and cold ischemia time were strongly related (Mishra et al., 2006). However, our data assessed that both urine and serum NGAL represent two significant and independent risk factors for DGF, even after adjusting for confounding variables, including cold ischemia time.
injury, we have also demonstrated that both uNGAL and sNGAL give prognostic information about long-term graft function. In fact, if predictive value of baseline eGFR confirms the general suggestion that an already impaired renal function is an important factor for the subsequent progression of renal damage, remarkably, both urinary and serum NGAL showed a most impressive predictive power in such a contest even after adjustment for eGFR. This suggests that NGAL would not be a simple surrogate index of baseline eGFR, but a marker on its own, predicting CAN progression beyond the information provided by GFR. Neutrophil gelatinase associated lipocalin (NGAL) levels could early identify those patients who are likely to benefit from conversion from CNI to alternative immunosuppressive regimen, such as sirolimus or everolimus. It was in fact demonstrated that the conversion of a CNI-based protocol to a minor nephrotoxic regimen seems to be an effective treatment in CAN (Citterlo et al., 2003; Kahan, 2000). Increased NGAL levels could precociously alert the clinicians. After the correction for potential confounding factor, such as urinary infections, delta values of this biomarker, compared with baseline levels and obtained at each visit, could give prognostic information. However, further studies are needed to confirm this timing and this hypothetical prognostic role of NGAL. Anyway, it is highly likely that not just one biomarker, such as NGAL, but rather a combination of biomarkers and clinical parameters will emerge as powerful tools for the early prediction of renal damage progression. In particular, our data demonstrated that also b-2 microglobulin represented an independent risk factor of CAN progression, as also confirmed by Astor who revealed that this medium molecule appeared to be a strong predictor of long-term mortality and graft loss in kidney transplant recipients (Astor et al., 2013). This study has some limitations that should be mentioned. The grafts came from different type of donors and, in CAN group, kidney transplant recipients were included at different times after transplant, suggesting potential heterogeneity of the cohort. It therefore is advisable to investigate the predictive capacity of biomarkers on kidney outcome at a fixed time after transplant. We did not examine other relevant biomarkers in parallel, which would have been valuable in the evaluation of the NGAL results. It was a hypothesis generating study, involving a relatively small group of patients. Confirmation in wider cohorts is indispensable to attribute general validity to our reports. However, the primary endpoints were reached by 55% (DGF) and 45% (CAN progression) of the participants, and the statistical model was powerful enough to establish independent relationships between NGAL, DGF and progression of renal disease. Finally, the index of doubling of serum creatinine, used here for comparison as one surrogate for GFR and renal disease progression, is itself an arbitrary measure with important limitations (Al-Aly, 2013). In conclusion, NGAL could be considered as a reliable predictor of DGF and tubular injury, in contrast with creatinine which is mainly related to filtration function disturbance. Moreover, it represents a prognostic biomarker,
NGAL and chronic allograft nephropathy progression If NGAL levels in the immediate post-transplant period accurately predict the duration and reversibility of acute
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giving information about chronic allograft nephropathy progression during a 5-year follow-up period.
Acknowledgements The authors appreciate the help in the collection of sample materials of Filippo La Spesa, Giorgio Damiani, Giuseppe Aiello and Rosario Lombardo, the nursing staff of the Nephrology ward of Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Palermo, Italy.
Declaration of interest The authors have no conflicts of interest to disclose that could be perceived as prejudicing the impartiality of the research reported. The authors have no financial relationships relevant to this article to disclose.
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References Al-Aly Z. (2013). Prediction of renal end points in chronic kidney disease. Kidney Int 83:189–91. Astor B, Muth B, Kaufman DB, et al. (2013). Serum b2-microglobulin at discharge predicts mortality and graft loss following kidney transplantation Kidney Int 84:810–17. Bolignano D, Donato V, Coppolino G, et al. (2008). Neutrophil gelatinase-associated lipocalin (NGAL) as a marker of kidney damage. Am J Kidney Dis 52:595–605. Bolignano D, Lacquaniti A, Coppolino G, et al. (2009). Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol 4:337–44. Bolignano D, Coppolino G, Donato V, et al. (2010). Neutrophil gelatinase-associated lipocalin (NGAL): a new piece of the anemia puzzle? Med Sci Monit 16:RA131–5. Buemi A, Musuamba F, Frederic S, et al. (2014). Is plasma and urine neutrophil gelatinase-associated lipocalin (NGAL) determination in donors and recipients predictive of renal function after kidney transplantation? Clin Biochem 47:68–72. Cernaro V, Bolignano D, Donato V, et al. (2011). NGAL is a precocious marker of therapeutic response. Curr Pharm Des 17:844–9. Citterlo F, Scata` MC, Violi P, et al. (2003). Rapid conversion to sirolimus for chronic progressive deterioration of the renal function in kidney allograft recipients. Transplant Proc 35:1292–4. Debout A, Foucher Y, Tre´bern-Launay K, et al. (2015). Each additional hour of cold ischemia time significantly increases the risk of graft failure and mortality following renal transplantation. Kidney Int 87: 343–9. Gourishankar S, Grebe SO, Mueller TF. (2013). Prediction of kidney graft failure using clinical scoring tools. Clin Transplant 27:517–22. Haase M, Bellomo R, Devarajan P, et al. (2009). NGAL Meta-analysis Investigator Group. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis 54:1012–24.
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Hariharan S, Johnson CP, Bresnahan BA, et al. (2000). Improved graft survival after renal transplantation in the United States, 1988 to 1996. N Engl J Med 342:605–12. Heyman SN, Rosenberger C, Rosen S. (2010). Experimental ischemiareperfusion: biases and myths-the proximal vs. distal hypoxic tubular injury debate revisited. Kidney Int 77:9. Hollmen ME, Kyllo¨nen LE, Inkinen KA, et al. (2011). Urine neutrophil gelatinase-associated lipocalin is a marker of graft recovery after kidney transplantation. Kidney Int 79:89–98. Kahan BD. (2000). Efficacy of sirolimus compared with azathioprine for reduction of acute renal allograft rejection: a randomised multicentre study. The Rapamune US Study Group. Lancet 356:194–202. Lachenbruch PA, Rosenberg AS, Bonvini E, et al. (2004). Biomarkers and surrogate endpoints in renal transplantation: present status and considerations for clinical trial design. Am J Transplant 4: 451–7. Lacquaniti A, Buemi F, Lupica R, et al. (2013a). Can neutrophil gelatinase-associated lipocalin help depict early contrast materialinduced nephropathy? Radiology 267:86–93. Lacquaniti A, Giardina M, Lucisano S, et al. (2013b). Neutrophil gelatinase-associated lipocalin (NGAL) and endothelial progenitor cells (EPCs) evaluation in aortic aneurysm repair. Curr Vasc Pharmacol 11:1001–10. Lacquaniti A, Donato V, Pintaudi B, et al. (2013c). ‘‘Normoalbuminuric’’ diabetic nephropathy: tubular damage and NGAL. Acta Diabetol 50:935–42. Levey AS, Bosch JP, Lewis JB, et al. (1999). A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 130:461–70. Mallon DH, Summers DM, Bradley JA, Pettigrew GJ. (2013). Defining delayed graft function after renal transplantation: simplest is best. Transplantation 96:885–9. Mishra J, Ma Q, Kelly C, et al. (2006). Kidney NGAL is a novel early marker of acute injury following transplantation. Pediatr Nephrol 21: 856–63. Mori K, Lee HT, Rapoport D, et al. (2005). Endocytic delivery of lipocalin-siderophore-iron complex rescues the kidney from ischemia reperfusion injury. J Clin Invest 115:610. Neri G, Lacquaniti A, Rizzo G, et al. (2012). Real-time monitoring of breath ammonia during haemodialysis: use of ion mobility spectrometry (IMS) and cavity ring-down spectroscopy (CRDS) techniques. Nephrol Dial Transplant 27:2945–52. Pennemans V, Rigo JM, Faes C, et al. (2013). Establishment of reference values for novel urinary biomarkers for renal damage in the healthy population: are age and gender an issue? Clin Chem Lab Med 51: 1795–802. Rossing P. (1998). Doubling of serum creatinine: is it sensitive and relevant? Nephrol Dial Transplant 13:244–6. Vanmassenhove J, Vanholder R, Nagler E, Van Biesen W. (2013). Urinary and serum biomarkers for the diagnosis of acute kidney injury: an in-depth review of the literature. Nephrol Dial Transplant 28:254–73. Yarlagadda SG, Coca SG, Garg AX, et al. (2008). Marked variation in the definition and diagnosis of delayed graft function: a systematic review. Nephrol Dial Transplant 23:2995.
ISSN: 1354-750X (Print) 1366-5804 (Online) Journal homepage: http://www.tandfonline.com/loi/ibmk20
Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin Antonio Lacquaniti, Chiara Caccamo, Paola Salis, Valeria Chirico, Antoine Buemi, Valeria Cernaro, Alberto Noto, Giuseppina Pettinato, Domenico Santoro, Tullio Bertani, Michele Buemi & Antonio David To cite this article: Antonio Lacquaniti, Chiara Caccamo, Paola Salis, Valeria Chirico, Antoine Buemi, Valeria Cernaro, Alberto Noto, Giuseppina Pettinato, Domenico Santoro, Tullio Bertani, Michele Buemi & Antonio David (2016) Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin, Biomarkers, 21:4, 371-378, DOI: 10.3109/1354750X.2016.1141991 To link to this article: http://dx.doi.org/10.3109/1354750X.2016.1141991
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Date: 11 May 2016, At: 23:09
http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2016; 21(4): 371–378 ! 2016 Taylor & Francis. DOI: 10.3109/1354750X.2016.1141991
RESEARCH ARTICLE
Delayed graft function and chronic allograft nephropathy: diagnostic and prognostic role of neutrophil gelatinase-associated lipocalin Antonio Lacquaniti1#, Chiara Caccamo2, Paola Salis2, Valeria Chirico3, Antoine Buemi4, Valeria Cernaro1, Alberto Noto5, Giuseppina Pettinato1, Domenico Santoro1, Tullio Bertani1, Michele Buemi1, and Antonio David5 Department of Internal Medicine, University Hospital of Messina, Messina, Italy, 2Department of Internal Medicine, Mediterranean Institute for Transplantation and Advanced Specialized Therapies, ISMETT, University of Pittsburgh Medical Center, Palermo, Italy, 3Department of Pediatric Science, University Hospital of Messina, Messina, Italy, 4Surgery and Abdominal Transplantation Division, Cliniques Universitaires Saint-Luc, Universite´ Catholique De Louvain, Brussels, Belgium, and 5Department of Neuroscience, University Hospital of Messina, Messina, Italy
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1
Abstract
Keywords
Context: Available markers are not reliable parameters to early detect kidney injury in transplanted patients. Objective: Examine neutrophil gelatinase associated lipocalin (NGAL) in early detection of delayed graft function (DGF) and as a long-term predictor of graft outcome. Patients and methods: NGAL was evaluated in 124 transplanted patients. Results: Urinary NGAL levels were associated to a 10% (HR: 1.10; 95% CI: 1.04–1.25; p50.001) and 15% (HR: 1.15; 95% CI: 1.09–1.26; p50.001) increased risk of DGF and allograft nephropathy progression, respectively. Conclusion: NGAL reflects the entity of renal impairment in transplanted patients, representing a biomarker and an independent risk factor for DGF and chronic allograft nephropathy progression.
Biomarker, chronic allograft nephropathy, delayed graft function, kidney transplantation, long-term graft failure, neutrophil gelatinase associated lipocalin
Introduction Early assessment of kidney function following renal transplantation is crucial for predicting graft survival, but not by serum creatinine, a not reliable parameter to detect acute kidney injury. In fact, the time relationship between changes in serum creatinine and concomitant changes in glomerular filtration rate (GFR) does not allow accurately estimating timing of injury and severity of dysfunction (Vanmassenhove et al., 2013). Furthermore, definitions of delayed graft function (DGF) are not standardized; in recent years, serum creatinine levels, its reduction rate and urine output have been used to identify DGF on different days following transplantation (Mallon et al., 2013). Several clinical algorithms have been proposed for the prediction of DGF based on pre-operative risk factors, but no objective tools are currently available (Gourishankar et al., 2013). However, 1-year graft survival after kidney transplant has greatly improved over the years, whereas long-term graft and
#Antonio Lacquaniti is responsible for statistical design/analysis. E-mail: [email protected] Address for correspondence: Dr Antonio Lacquaniti, Department of Internal Medicine, 98100 Messina, Italy. Tel: +39 090 2212396. E-mail: [email protected]
History Received 8 June 2015 Revised 7 January 2016 Accepted 11 January 2016 Published online 11 February 2016
patient survival has changed to only a limited extent (Hariharan et al., 2000; Lachenbruch et al., 2004). As chronic transplant dysfunction is a predominantly tubular-interstitial process in kidney transplant recipients, specific tubular damage markers, such as kidney injury molecule-1 (KIM-1) or neutrophil gelatinase-associated lipocalin (NGAL), could be better predictors of kidney function outcome. In particular, NGAL is a protein belonging to the lipocalin superfamily produced by activated neutrophils. It has been shown that many other cells, including kidney tubule, may be involved in NGAL production, in response to several injuries, such as ischemia-reperfusion, sepsis or toxic stress (Bolignano et al., 2008, 2010). Haase confirmed the predictive and prognostic value of NGAL as an early biomarker for acute damage in a metaanalysis involving more than 2500 patients (Haase et al., 2009). Furthermore, NGAL evaluation was also applied in other several clinical settings of acute kidney injury, such as contrast induced nephropathy or after major surgery (Lacquaniti et al., 2013a,b). In the field of kidney transplantation, numerous parameters have been used to predict graft survival, including NGAL (Buemi et al., 2014; Hollmen et al., 2011; Neri et al., 2012). However, while few studies demonstrated its utility in predicting DGF, it is unknown whether it would predict long-term allograft function after renal transplantation.
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Several data have in fact showed that NGAL represents not only a biomarker of acute renal injury, but also it plays a diagnostic and prognostic role in chronic kidney diseases (Bolignano et al., 2009; Lacquaniti et al., 2013c). Moreover, Pennemans analyzing urine NGAL values in healthy subjects, proposed physiological cut-off values, according to gender and age. It was in fact demonstrated that urine NGAL is closely related to patient’s age and sex, also after its correction for creatinine and urine specific gravity (Pennemans et al., 2013). In this study, we examined if urine and serum NGAL could detect acute kidney damage after renal transplantation, assessing its role to early reveal DGF. We have also evaluated the role of NGAL as long-term predictor of graft outcome.
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Materials and methods
Biomarkers, 2016; 21(4): 371–378
Collection of blood and NGAL evaluation In DGF group, serum and urine samples were collected preoperatively (baseline data) and at the first post-operative day (POD 1). Neutrophil gelatinase associated lipocalin (NGAL) was measured in blood and urine using ELISA commercial available kit (Antibody Shop, Gentofte, Denmark) according to manufacturer’s instructions. All specimens were often diluted to obtain concentration for the optimal density according to ELISA kit instructions. The enzymatic reactions were quantified in an automatic microplate photometer. Serum (sNGAL) and urinary NGAL (uNGAL) levels were expressed as ng/ml. In CAN group, sNGAL and uNGAL levels were evaluated at the enrollment phase. Patients were followed every 3 months, through the evaluation of renal function tests and other common biochemical parameters, including 24-h urine samples.
Patients and study design
Definitions and follow-up period
A prospective and observational study was designed, involving 124 patients. In particular, 40 transplanted patients, 418 years of age, who received a kidney from deceased and living donors at the Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Palermo, Italy, were enrolled to evaluate delay graft function (DGF group). To minimize potential confounding factors, exclusion criteria were neoplastic diseases, pregnancy, cardio-renal syndrome, acute hepatitis and cirrhosis, recent stroke. We excluded patients who had a primary graft failure related to surgical/anatomic causes. The immunosuppressive regimen was similar in all patients, consisting of pre-operative basiliximab and post-operative tacrolimus with prednisone and mycophenolate mofetil. Eighty-four renal transplanted patients who were referred to the renal transplant outpatient clinic of the Nephrology and Dialysis Unit of Messina University Hospital were enrolled to assess the chronic allograft nephropathy progression (CAN group). In particular, patients with 12 months or more renal transplantation were eligible to begin a 5-year observational study. The study recruited male or female patients aged 18–70 years who received first or second kidney transplant from a deceased (CTx), living-related (LTx) or living-unrelated donor, with panel reactive antibodies (PRA) below 20% at the last pre-transplant assessment. Patients were excluded if they received multi-organ transplant (including two kidneys) or previous non-renal transplant. Patients with serum creatinine above 6 mg/dl and/or estimated GFR515 ml/min, malignancy, liver, thyroid or infectious diseases, severe proteinuria (43 g/die), active inflammatory states, moderate-severe heart disease were not recruited. The Ethics Committee and the Institutional review board approved the study. The research was performed in accordance with the Declaration of Helsinki. The clinical and research activities have been consistent with the Principles of the Declaration of Istanbul. All patients gave written informed consent.
Cold ischemia time (CIT) was defined as the duration between the beginning of cold storage and reperfusion of the graft. CIT was grouped as 0–15 h and 415 h for analysis. Delayed graft function (DGF) was defined by need for dialysis within the first week after transplantation, or when serum creatinine level increased, remained unchanged, or decreased by less than 10% per day immediately after surgery (Yarlagadda et al., 2008). Glomerular filtration rate (GFR) post-transplantation time points were calculated using the modification of diet in renal disease (MDRD) equation (Levey et al., 1999). According with these definitions, patients were separated into two groups: DGF group and immediate graft function (IGF) group. Chronic allograft nephropathy progression (CAN) group patients were followed prospectively until the end of 5-year period or the primary study endpoint was reached. This latter was defined by the loss of the renal graft, defined as a re-start of dialytic treatment, a decline of the GFR515 ml/min, a re-transplant or a reduction in kidney function revealed by a doubling of serum creatinine, an accepted surrogate index of GFR slope (Rossing, 1998). Statistical analyses Statistical analyses were performed with NCSS for Windows version 4.0, the MedCalc version 11.0 (MedCalc Software Acacialaan, Ostend, Belgium) software and the GraphPad Prism version 5.0 (GraphPad Software, Inc., San Diego, CA) package. Data were presented as mean ± SD for normally distributed values and median [IQ range] for non-normally distributed values. Differences between groups were established by unpaired t test or by ANOVA followed by Bonferroni’s test for normally distributed values and by the Kruskal–Wallis analysis followed by Dunn’s test for nonparametric values. Dichotomized values were compared using the 2 test. Correlation coefficients were used as appropriate to test correlations between NGAL and other variables. Receiver-operating characteristics (ROC) analysis was employed to calculate the area under the curve (AUC) for serum and urine NGAL, creatinine and urinary output to find
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the best cut off values capable for identifying DGF and the progression to renal endpoint. Kaplan–Meier curves were generated to assess renal survival. Differences in the survival rates were evaluated using the log-rank test. Adjusted risk estimates for progression endpoint were calculated using univariate followed by multivariate Cox proportional hazard regression analysis. All results were considered significant if p was50.05.
Results NGAL as a diagnostic marker of DGF
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Patients characteristics at baseline and after kidney transplant In DGF group, 30 (75%) patients received hemodialysis whereas 3 (7%) received peritoneal dialysis in the pre-surgery period. Seven (18%) patients did not receive pre-transplantation dialysis. The mean time on dialysis was 27 ± 4.4 months. Living donor transplantation accounted for 27% (11 patients), whereas 29 patients (73%) underwent deceased kidney transplant. The cold ischemia time was 12.9 ± 5.4 h. Twenty-eight patients (69%) transplanted within 15 h, whereas the remaining (12 patients; 31%) received the kidney after a cold ischemia time 415 h. Delayed graft function (DGF) was encountered in 22 (55%) recipients, while no LTx patients had a DGF event after transplantation. At baseline, before the transplantation, sNGAL level was 386.9 ± 39.9 ng/ml, with no differences between CTx and LTx (387.1 ± 43 versus 374.1 ± 20.1 ng/ml, respectively; p ¼ 0.20). At POD 1, NGAL levels were lower in LTx than CTx patients (sNGAL: 223.1 ± 86.7 versus 392.1 ± 225.3 ng/ml; p50.001; uNGAL: 39.6 ± 13.9 versus 172.5 ± 117.7 ng/ml; p50.001). Moreover, sNGAL and uNGAL levels were higher in patients who received a deceased donor kidney and subsequently developed delayed graft function (DGF group: 520.7 ± 318 ng/ml and 135.8 ± 93.4, respectively), compared with CTx patients with prompt graft function (IGF group: 287.8 ± 162.2 ng/ml; p50.001 and 47.4 ± 40.3; p50.001). Figure 1 summarizes these findings. Serum creatinine was not different between IGF and DGF groups at POD 1 (p ¼ 0.10), whereas it was higher in DGF group than IGF patients only after 3 days from renal transplant (p50.001). A close correlation between NGAL and renal function parameters, such as creatinine and eGFR, was assessed. Moreover, a strict and direct correlation between uNGAL and CIT was revealed. Table 1 demonstrates clinical characteristics of donors and recipients, at baseline and after kidney transplantation. ROC analyses Receiver operating characteristics (ROC) analyses were performed in order to define the diagnostic profile of NGAL in identifying DGF among transplanted patients. The AUC for uNGAL was 0.979. Moreover, a cut-off value of uNGAL set at 105 ng/mL was associated to a sensitivity and specificity of 95.8 and 91.9%.
Figure 1. Serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) levels in transplanted patients enrolled for DGF evaluation. (A) While no differences were assessed in pre-transplant period (NS), both serum and urine NGAL levels were reduced in related living transplant patients (LTx) in the first post-operative day. (B) Serum and urine NGAL levels were higher in patients who received a deceased donor kidney (CTx) and subsequently developed delayed graft function (DGF group), compared with CTx patients with prompt graft function (IGF group).
Diagnostic performances of uNGAL were better than creatinine (AUC: 0.676; p50.001), whereas no differences have been revealed between creatinine and sNGAL. Urine output of the first 24 h was characterized by an AUC of 0.880, with a more sensitivity and specificity than creatinine and sNGAL (p50.001 and 0.002, respectively), but less than uNGAL (p ¼ 0.02) to detect DGF (Figure 2). Univariate/multiple Cox regression analysis and risk of DGF To identify putative risk factors associated with DGF, we performed a Cox regression analysis, inserting in the model all variables that were different at first post-operative day in patients who had or not a DGF. Univariate analysis showed that only donor age, donor creatinine, CIT, HLA mismatch, residual diuresis, sNGAL and uNGAL were significantly associated with endpoint, whereas recipient age and time on dialysis failed to reach statistical significance. A multiple Cox regression indicated that both uNGAL and sNGAL predicted high risk of DGF, independently of others variables. In detail, the increase of uNGAL was associated with a 10% increased risk of DGF (HR 1.10; 95% CI: 1.04–1.25; p50.001), whereas the increase of sNGAL
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Table 1. Demographic and clinical characteristics in transplant recipients and donors.
Deceased donor Age (years) Gender, male/female Inotropic support, n Hypertension, n (%) Diabetes mellitus Terminal serum creatinine CIT, h Recipient Age, years Gender, male/female BMI, kg/m2 Diabetes mellitus Blood Hypertension, n (%) HLA mismatch 5 3, n (%) HLA mismatch 3, n (%) Time on dialysis, months Residual diuresis, ml/24 h Baseline creatinine, mg/dl Baseline eGFR, ml/min POD 1 sNGAL, ng/ml POD 1 uNGAL, ng/ml
Total (n ¼ 29)
DGF (n ¼ 22)
No DGF (n ¼ 7)
p
47.3 ± 20.8 17/12 18 8 (27) 0 1.21 ± 0.6 12.9 ± 5.4
63.8 ± 7.7 13/9 13 6 – 1.3 ± 0.7 16.1 ± 4.1
28.4 ± 12.9 4/3 5 2 – 0.8 ± 0.2 7.6 ± 1.5
50.0001 0.34 0.57 0.94 – 0.007 50.0001
45.2 ± 18.3 15/14 23 ± 5.5 4 (14%) 15 (51%) 10 (34%) 19 (66%) 27 ± 4.4 311.1 ± 428.1 8.7 ± 1.2 8.6 ± 1.2 392.1 ± 225.3 172.5 ± 117.7
58.7 ± 7.8 15/7 23.1 ± 5.1 3 11 4 (18%) 18 (82%) 29.0 ± 3.1 214.2 ± 413.8 8.6 ± 1.2 8.5 ± 2.5 520.7 ± 318 135.8 ± 93.4
27.3 ± 9.8 2/5 22.6 ± 7.3 1 4 6 (85%) 1 (15%) 21.7 ± 2.5 700 ± 305.5 9.0 ± 1.5 9.0 ± 1.6 287.8 ± 162.2 47.4 ± 40.3
0.0002 0.41 0.92 0.96 0.75 0.0007 0.0004 50.0001 0.005 0.48 0.61 0.007 0.0003
DGF: delayed graft function; CIT: cold ischemia time; BMI: body mass index; HLA: human leukocyte antigen; eGFR: estimated glomerular filtration rate; POD: post-operative day; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
amplified this risk by 6% (HR 1.06; 95% CI: 1.03–1.12; p50.001). Table 2 summarizes the Cox analysis. NGAL as a prognostic marker for allograft nephropathy progression NGAL evaluation
Figure 2. Receiver operating characteristics curves of serum neutrophil gelatinase-associated lipocalin (sNGAL), urinary NGAL (uNGAL), serum creatinine and urinary output considering delayed graft function (DGF) as status variable. The area under the curve for sNGAL, uNGAL, serum creatinine and urinary output was 0.67 (95% CI: 0.54–0.79), 0.97 (95% CI: 0.90–0.99), 0.67 (95% CI: 0.54–0.79) and 0.88 (95% CI: 0.77–0.94), respectively. The best cut-off value to predict DGF for uNGAL was found to be 105 ng/ml, with a sensitivity of 95.8 (95% CI: 78.9–99.9) and a specificity of 91.9 (95% CI: 78.1–98.3), whereas for urinary output it was 15 ml/h with a sensitivity of 64.0 (95% CI: 42.5– 82.0) and a specificity of 97.3 (95% CI: 85.8–99.9). Urine output of the first 24 h was characterized by a more sensitivity and specificity than creatinine and sNGAL (p50.001 and p ¼ 0.002, respectively), but less than uNGAL (p ¼ 0.02) to detect DGF.
Eighty-four renal transplanted patients were enrolled to assess chronic allograft nephropathy progression (CAN group). Mean serum creatinine at baseline was 1.53 ± 0.7 mg/dl, with a mean eGFR of 49.7 ± 21 ml/min/1.73 m2. According to Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines based on eGFR, 18 patients (22%) belonged to stages I– II, 45 subjects (53%) to stage III and 21 (25%) patients to stage IV. Mean value of sNGAL was 137.7 ± 64.6 ng/ml, whereas uNGAL levels were 88.3 ± 72.5 ng/ml, both gradually rising according to CKD stages. In particular, the highest values, both for urine and serum NGAL, were assessed in patients belonging to stage IV (sNGAL: 197 ± 43 ng/ml; uNGAL: 112.2 ± 83 ng/ml). During the observational period, 38 patients (45%) reached the renal endpoint. In particular, 27 patients had a progression of renal disease, whereas 11 patients experienced a severe worsening in renal function, requiring dialytic treatment. Table 3 displays main data and statistical differences between patients with or without renal disease progression. ROC analysis Receiver operating characteristics (ROC) analyses were performed in order to define the diagnostic profiles of NGAL in identifying a worsening of renal function among
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Table 2. Univariate and multivariate Cox proportional hazards regression model for incidence of delayed graft function. Univariate analysis
Donor age Donor creatinine CIT HLA mismatch Recipient age Time on dialysis Residual diuresis sNGAL uNGAL
Multivariate analysis
HR
95% CI
p
HR
95% CI
p
1.03 1.02 1.11 1.09 1.06 1.15 1.02 1.04 1.08
1.01–1.06 1.01–1.03 1.03–1.21 1.05–1.17 0.97–1.08 0.73–1.18 1.01–1.04 1.01–1.08 1.08–1.22
0.02 0.03 0.002 0.001 0.12 0.23 0.04 0.001 0.004
1.02 1.03 1.14 1.08
1.01–1.03 0.96–1.03 1.05–1.27 1.04–1.10
0.01 0.10 0.004 0.01
1.01 1.06 1.10
0.97–1.04 1.03–1.12 1.04–1.25
0.09 0.006 0.0004
CIT: cold ischemia time; HLA: human leukocyte antigen; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
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Table 3. Baseline characteristics and long-term follow up in chronic allograft nephropathy group.
Recipients age, years Gender, male/female Diabetes mellitus, n (%) Hypertension, n (%) BMI, kg/m2 Months after Txa Living donation, n (%) Donor age, years CIT, h HLA mismatch 3 DGF, n (%) Acute rejection, n (%) Serum creatinine, mg/dl eGFR, ml/min b2 microglubulin Proteinuria, mg/24 h sNGAL, ng/ml uNGAL, ng/ml
Baseline (N ¼ 84)
Progressor (N ¼ 38)
Non-progressor (N ¼ 46)
p
56.1 ± 11.8 46/38 23 (27%) 68 (80%) 26.1 ± 2.3 22 (16–36) 22 (26) 58.3 ± 16.3 14.2 ± 6.3 31 (37%) 48 (57%) 56 (66%) 1.53 ± 0.7 49.7 ± 21 4727.9 ± 2985 852.3 ± 622.7 137.7 ± 64.6 88.3 ± 72.5
65.2 ± 5.7 21/17 13 (34) 33 (86%) 25.9 ± 2.5 48 (19–55) 6 (15) 65.9 ± 2.9 16.7 ± 1.6 20 (52) 29 (76) 29 (76) 1.75 ± 1.1 25.9 ± 10.2 4876.8 ± 1476.8 1090 ± 667.8 154.5 ± 52.5 128 ± 34.2
47 ± 8.6 25/21 10 (21) 35 (76) 26.3 ± 2.1 21.5 (16–29.5) 16 (35) 53.4 ± 14.4 11.9 ± 3.2 11 (24) 19 (41) 27 (58) 1.41 ± 0.3 53.5 ± 13.9 2294.6 ± 851.3 614 ± 475.4 109.7 ± 47.6 78.7 ± 44.9
0.0002 0.16 0.84 0.21 0.80 0.03 0.04 0.0008 50.0001 0.006 0.001 0.09 0.38 0.0006 0.0003 0.003 0.004 50.0001
a
Expressed as median (IQ range). BMI: body mass index; CIT: cold ischemia time; HLA: human leukocyte antigen; DGF: delayed graft function; eGFR: estimated glomerular filtration rate; sNGAL: serum neutrophil gelatinase associated lipocalin; uNGAL: urine neutrophil gelatinase associated lipocalin.
transplanted patients. The AUC for uNGAL was 0.889 with a sensitivity and specificity of 71.8 and 100% with a cut-off value set at 97 ng/ml. A comparison of ROC curves was also made. Whereas the difference between sNGAL and eGFR areas was not significant (p ¼ 0.68), uNGAL area was statistically different than that of eGFR (p ¼ 0.002) and sNGAL (p ¼ 0.002) (Figure 3). Kaplan–Meier curves were generated to assess renal survival in subjects with serum and urine NGAL values above and below the optimal ROC-derived cutoff levels. Subjects with sNGAL values above 122 ng/ml showed a significantly faster progression to endpoint (p ¼ 0.03), with a hazard ratio of 2.03 (95% CI: 1.06–3.89). Similarly, subjects with uNGAL values above 97 ng/ml experienced a significantly faster evolution to endpoint, with a mean follow-up time to progression of 32 months [log-rank ( 2):8.03; p50.001; hazard ratio: 2.7 (95% CI:1.3–5.4)] compared with460 months for uNGAL below the cut-off (Figure 4).
Univariate/multiple Cox regression analysis To identify putative risk factors associated with incidence of CAN progression, we performed the Cox regression analysis. Univariate analysis showed that only recipients age, HLA mismatch number, DGF, eGFR, b2 microglobulin, proteinuria, sNGAL and uNGAL were significantly associated with endpoint, whereas donors age and source, months after KTx and cold ischemia time failed to reach statistical significance. A multiple Cox regression was constructed, indicating that high levels of uNGAL were associated with a 15% increased risk of CAN progression (HR 1.15; 95% CI:1.09–1.26; p50.001), whereas the raise of sNGAL increased this risk by 8% (HR 1.08; 95% CI: 1.03–1.10; p ¼ 0.02) (Table 4).
Discussion Findings from this study clearly indicate that both serum and urine NGAL evaluation could be particularly useful in kidney allograft recipients, a nephropatic population always in
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balance between acute kidney injury risk and chronic renal damage progression. NGAL and delayed graft function
In fact, we demonstrated that DGF patients were characterized, since the first day after transplantation, by high levels of NGAL, in urine and serum. Therefore, if excellent allograft function occurred, as assessed in LTx patients, NGAL values
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Delayed graft function (DGF) reflects a post-ischemic acute tubular necrosis and all transplanted grafts, including IGF patients, are susceptible to ischemia-reperfusion injury (Heyman et al., 2010). NGAL represents a very precocious marker of DGF, predicting the evolution of graft function.
Figure 3. Receiver operating characteristics curves of eGFR, serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) considering progression of chronic allograft nephropathy as status variable. The area under the curve for eGFR, sNGAL and uNGAL was 0.64 (95% CI: 0.53–0.75), 0.62 (95% CI: 0.50–0.73) and 0.89 (95% CI: 0.79–0.95), respectively. As the difference between sNGAL and eGFR areas was not significant (p ¼ 0.68), uNGAL area was statistically different than that of eGFR (p ¼ 0.002) and sNGAL (p ¼ 0.002). For uNGAL, a sensitivity and specificity of 71.8 and 100%, respectively, were obtained with a cut off value set at 97 ng/ml.
Figure 4. Kaplan–Meier survival curves of renal end-point in CAN patients with serum neutrophil gelatinase-associated lipocalin (sNGAL) and urinary NGAL (uNGAL) levels above and below the optimal receiver operating characteristics cut-off levels. (A) Patients with sNGAL 4122 ng/ml showed a significantly faster progression to endpoint (p ¼ 0.003, log-rank test), with a hazard ratio of 2.03 (95% CI: 1.06–3.89). (B) Subjects with uNGAL values above 97 ng/ml experienced a significantly faster evolution to endpoint, with a mean follow-up time to progression of 32 months [p50.001 log-rank test), witha hazard ratio of 2.7 (95% CI: 1.3–5.4).
Table 4. Univariate and Multivariate Cox proportional hazards regression model for incidence of chronic allograft nephropathy progression during a 5-year follow up period. Univariate analysis
Donors age Donors source Recipients age months after KTx HLA mismatcha Cold ischemia time DGF eGFR b2 microglobulin Proteinuria sNGAL uNGAL a
Multivariate analysis
HR
95% CI
p
1.03 1.10 1.05 1.06 1.10 1.04 1.06 0.83 1.12 1.05 1.06 1.10
0.91–1.06 0.95–1.21 1.02–1.10 0.97–1.18 1.03–1.18 0.96–1.08 1.01–1.13 0.74–0.96 1.06–1.17 1.01–1.07 1.01–1.10 1.08–1.22
0.11 0.32 0.01 0.37 0.001 0.10 0.01 0.002 0.001 0.03 0.01 0.001
HR
95% CI
p
1.03
0.97–1.04
0.08
1.08
1.04–1.10
0.01
1.02 0.94 1.15 1.02 1.08 1.15
1.01–1.04 0.89–0.97 1.07–1.20 0.97–1.04 1.03–1.10 1.09–1.26
0.03 0.03 0.002 0.10 0.02 0.003
Risk associated with HLA mismatch number43. KTx: kidney transplantation; HLA: human leukocyte antigen; CIT: cold ischemia time; DGF: delayed graft function; eGFR: estimated glomerular filtration rate; uNGAL: urine neutrophil gelatinase associated lipocalin; sNGAL: serum neutrophil gelatinase associated lipocalin.
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DOI: 10.3109/1354750X.2016.1141991
Delayed graft function and chronic allograft nephropathy
decreased quickly during first 24 h post-transplant period, reflecting a more pronounced reversible short-term injury. It is important to underline that, in the immediate postoperative period, urine output may induce uNGAL dilution. In fact, a living related kidney transplant recipient, in the first hour after operation, has often a significant urine output and thus baseline post-transplant uNGAL results may be underestimated as a result of urinary dilution. However, as our ROC analysis showed, uNGAL presented a better diagnostic profile than sNGAL and urine output, reflecting a major involvement of kidney tubules than prerenal sources. Thus, urinary biomarker on POD1 not only predicts who will need dialysis within 1 week, but also could discriminate between more subtle allograft recovery patterns. Although there is no specific treatment for DGF, recognizing precociously patients who have an increased risk of developing DGF helps clinicians to optimize postoperative care and avoid nephrotoxic treatments and interventions. Neutrophil gelatinase associated lipocalin (NGAL) levels could guide perioperative fluid management, ensuring an adequate restoration and maintenance of the intravascular volume, according to renal tubule function. In fact, as an aggressive hydration has been recognized to be effective in avoiding DGF, fluid overload may also precipice the need of dialysis with the risk of hypovolemia and consequent renal ischemia. Moreover, NGAL values could guide the immunosuppressive strategy, as well as the induction protocol, the timing of steroid withdrawal in the first week after transplantation, or avoid or delay the introduction of calcineurin inhibitors. Furthermore, biopsy timing and post-biopsy therapeutic decisions could be based on NGAL levels, choosing more aggressive strategies in patients with high serum and urine NGAL values. Daily measurements of this biomarker could also give prognostic information, acting as a marker of therapy response. Further studies are needed to confirm this timing and to validate these suggested roles of NGAL. The closely relation between uNGAL and renal tubule was also demonstrated by Mori who revealed that clinical tubulitis Banff Ia/Ib and other clinical tubular pathologies caused significantly higher levels of NGAL than those assessed in stable transplants with normal tubular histology or stable transplants with subclinical tubulitis (Cernaro et al., 2011; Mori et al., 2005). Our data revealed that only uNGAL has been closely related to cold ischemia time, one of the main modifiable pretransplantation risk factors, responsible of DGF and graft survival (Debout et al., 2015). This datum was also confirmed by in vitro studies, in which NGAL staining and cold ischemia time were strongly related (Mishra et al., 2006). However, our data assessed that both urine and serum NGAL represent two significant and independent risk factors for DGF, even after adjusting for confounding variables, including cold ischemia time.
injury, we have also demonstrated that both uNGAL and sNGAL give prognostic information about long-term graft function. In fact, if predictive value of baseline eGFR confirms the general suggestion that an already impaired renal function is an important factor for the subsequent progression of renal damage, remarkably, both urinary and serum NGAL showed a most impressive predictive power in such a contest even after adjustment for eGFR. This suggests that NGAL would not be a simple surrogate index of baseline eGFR, but a marker on its own, predicting CAN progression beyond the information provided by GFR. Neutrophil gelatinase associated lipocalin (NGAL) levels could early identify those patients who are likely to benefit from conversion from CNI to alternative immunosuppressive regimen, such as sirolimus or everolimus. It was in fact demonstrated that the conversion of a CNI-based protocol to a minor nephrotoxic regimen seems to be an effective treatment in CAN (Citterlo et al., 2003; Kahan, 2000). Increased NGAL levels could precociously alert the clinicians. After the correction for potential confounding factor, such as urinary infections, delta values of this biomarker, compared with baseline levels and obtained at each visit, could give prognostic information. However, further studies are needed to confirm this timing and this hypothetical prognostic role of NGAL. Anyway, it is highly likely that not just one biomarker, such as NGAL, but rather a combination of biomarkers and clinical parameters will emerge as powerful tools for the early prediction of renal damage progression. In particular, our data demonstrated that also b-2 microglobulin represented an independent risk factor of CAN progression, as also confirmed by Astor who revealed that this medium molecule appeared to be a strong predictor of long-term mortality and graft loss in kidney transplant recipients (Astor et al., 2013). This study has some limitations that should be mentioned. The grafts came from different type of donors and, in CAN group, kidney transplant recipients were included at different times after transplant, suggesting potential heterogeneity of the cohort. It therefore is advisable to investigate the predictive capacity of biomarkers on kidney outcome at a fixed time after transplant. We did not examine other relevant biomarkers in parallel, which would have been valuable in the evaluation of the NGAL results. It was a hypothesis generating study, involving a relatively small group of patients. Confirmation in wider cohorts is indispensable to attribute general validity to our reports. However, the primary endpoints were reached by 55% (DGF) and 45% (CAN progression) of the participants, and the statistical model was powerful enough to establish independent relationships between NGAL, DGF and progression of renal disease. Finally, the index of doubling of serum creatinine, used here for comparison as one surrogate for GFR and renal disease progression, is itself an arbitrary measure with important limitations (Al-Aly, 2013). In conclusion, NGAL could be considered as a reliable predictor of DGF and tubular injury, in contrast with creatinine which is mainly related to filtration function disturbance. Moreover, it represents a prognostic biomarker,
NGAL and chronic allograft nephropathy progression If NGAL levels in the immediate post-transplant period accurately predict the duration and reversibility of acute
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giving information about chronic allograft nephropathy progression during a 5-year follow-up period.
Acknowledgements The authors appreciate the help in the collection of sample materials of Filippo La Spesa, Giorgio Damiani, Giuseppe Aiello and Rosario Lombardo, the nursing staff of the Nephrology ward of Mediterranean Institute for Transplantation and Advanced Specialized Therapies, Palermo, Italy.
Declaration of interest The authors have no conflicts of interest to disclose that could be perceived as prejudicing the impartiality of the research reported. The authors have no financial relationships relevant to this article to disclose.
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